Fuzzy methods for automated inspection of food products

Abstract

Automated product inspection is of considerable interest to food manufacturers since human inspectors currently perform a substantial amount of on-line inspection. At a low-level of information processing, machine vision offers advantages of objective and consistent assessment. However machine vision systems are frequently used for grading and quality control. In these applications, it is necessary to integrate a number of physical features to make an inference about overall quality that is consistent with consumer judgements. The work presented in this paper focuses on quality assessment of chocolate chip cookies based solely on visual features. Digital images were used to define physical characteristics of cookies produced on a commercial bakery line. Consumers were asked to rate typical cookies on a line scale. A number of fuzzy systems were developed to make quality control decisions based on features extracted from digital images. Results from two fuzzy systems are compared to consumer results from a validation test.